Photoplethysmogram (PPG) signal reacts according to the volumetric change of blood, that is related to the total cardiovascular system. Hence PPG signals have a very close relation with cardiovascular diseases. As the number of cardiovascular disease (CVD) patients is increasing day by day, it is necessary to develop digital systems for early detection and treatment. In this study, we have aimed to design a field programmable gate array (FPGA) based hardware system that can detect various CVDs. We have chosen six different combinations of diseases from the PPG BP dataset. The selected signals are denoised using filters, and then features are extracted. Then SVM classifier is used to determine the class of the preprocessed signals. For FPGA implementation, we have targeted Xilinx zynq ultrascale+. The system utilizes different logic and signal processing blocks to detect the result. The resource utilization analysis proves the efficacy of the selected FPGA board. The power utilization analysis depicts that the designed prototype requires 1.403 W of power, which is sensible due to the requirement of large number of logic blocks. The accuracy of the system of detecting multiclass diseases is 79.83%. In the future, the system can be further extended to detect other types of CVDs.

The demand for renewable sources-based micro-grid systems is increasing all over the world to address the United Nation’s (UN) sustainable development goal 7 (SDG7) “affordable and clean energy”. However, without proper viability analysis, these micro-grid systems might lead to economic losses to both customers and investors. Therefore, this paper aims to explore the feasibility and sustainability of a hybrid micro-grid system based on available renewable resources in remote hill tracts region of Bangladesh. Nine different scenarios are analyzed here, and a combination of solar, hydro, biogas, and diesel generator systems are found to be the best feasible solution in regard to the least cost of electricity and emission. The optimized result shows that with a renewable fraction of 0.995, the unit levelized cost of energy of the micro-grid system is $0.182 and it emits 54 and 117 times less CO2 compared to grid-based and diesel-based systems. Further, the fuel share of the system being 0.5% and greenhouse gas per energy being 0.06425 kg/KWh, validate the system as highly sustainable and eco-friendly. With the ability to fulfill load demands without interrupting supply, and reducing the emissions of greenhouse gases, the designed microgrid can provide sustainable energy solutions to any hill-tracts of Bangladesh.

This research investigates a dual-polished surface plasmon resonance sensor based on dual-core photonic crystal fiber, featuring an innovative design aimed at enhancing hemoglobin concentration detection in blood, providing a valuable tool for diagnosing numerous health issues, such as chronic obstructive pulmonary disease. The sensor makes use of an external sensing mechanism and utilizes gold (Au) coating as the plasmonic material, chosen for its strong plasmonic response and excellent chemical stability, ensuring robust performance across the 1.31–1.42 refractive index range. The electromagnetic characteristics and efficacy of the designed sensor were thoroughly investigated using the finite element method. Our proposed sensor demonstrates outstanding performance metrics, attaining peak amplitude sensitivity of about 734 RIU⁻¹, and wavelength sensitivity of 74,000 nm/RIU along with 1.35 × 10⁻⁶ RIU wavelength resolution. It also exhibits a notable Figure of Merit value of 667 for a corresponding Full width at Half Maximum value of 111 nm. Finally, a machine learning model based on linear regression was employed that enables the prediction of any hemoglobin concentration levels corresponding to analyte RI values. These exceptional performance metrics highlight the potential of our sensor as a reliable, cost-effective and highly sensitive solution for real-time biosensing applications.